Currently, end users regularly utilize smartphones, voice over internet protocol (VoIP) applications, and other audio-based technologies to place and receive phone calls, access various types of services and content, perform a variety of functions, or a combination thereof. As the importance and prevalence of mobile communications has increased, end users have become increasingly likely to utilize mobile communications devices and voice-based applications in environments that include significant amounts of ambient noise that may potentially interfere with the end users' communications. In particular, such ambient noise may interfere with the perceptibility and quality of communications held between end users, communications intended for automatic speech recognition systems, communications intended for various voice-based applications, other types of communications, or any combination thereof.
Traditionally, adaptive filtering has been utilized to filter ambient noise obtained by one or more microphone sensors positioned in a home, car, or other similar location. While adaptive filtering assists in providing noise suppression for various types of audio communications, adaptive filtering requires a noticeable amount of adaptation time for a speech enhancement system to determine what the actual type of acoustic environment based on its acoustic parameters. Additionally, during the adaptation time, any speech or noise enhancement that can be provided during the adaptation period is minimal, if any. Furthermore, studies have shown that more than half of all commands entered into various automatic speech recognition systems are very short. As a result, the adaptation time required in existing speech enhancement systems does not allow for the enhancement of the audio associated with the first few commands or words spoken into an automatic speech recognition system. Moreover, traditional solutions typically involve brute-force processing, in isolation, of all of the various audio information occurring in the environment. Such brute-force processing often requires extensive use of limited network resources, causes communication delays, increases power usage, and increases network and other costs.